Units, Currency, and Time: The Hidden Semantics That Cause Bad Answers

TL;DR

• Hidden units create wrong comparisons.
• Make units and time basis explicit in names and metadata.

The problem

• Measures can represent dollars, percentages, or counts without stating which.
• Currency conversions or time windows are embedded in logic.

Why it matters

• AI may compare incompatible values or summarize incorrectly.
• Business decisions rely on correct unit semantics.

Symptoms

• Percentages treated as counts in explanations.
• Global revenue compared to local currency costs.

Root causes

• Unit details captured in documentation, not in the model.
• Currency normalization done inconsistently.

What good looks like

• Units and currencies encoded in measure names.
• Time windows explicitly documented and standardized.

How to fix

• Append unit and currency suffixes (e.g., Revenue_USD).
• Standardize time windows in metric definitions.
• Add metadata notes about conversion rules.

Pitfalls

• Assuming the visualization implies the unit.
• Mixing multiple time windows in one calculation.

Checklist

• Every KPI includes unit and currency in metadata.
• Time windows are explicit and documented.
• Conversions are centralized.

Framework placement

Primary ARF layer: Semantic Integrity. Diagnostic bridge: semantic-reliability, change-reliability.